- Other dimensionality reduction like t-stochastic neighbor embedding(tSNE) or Uniform Manifold Approximation and Projection(UMAP)
- UMAP has become the standard
pbmc <- RunUMAP(pbmc, dims = 1:10)# only first 10
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 01:38:21 UMAP embedding parameters a = 0.9922 b = 1.112
## 01:38:21 Read 2694 rows and found 10 numeric columns
## 01:38:21 Using Annoy for neighbor search, n_neighbors = 30
## 01:38:21 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 01:38:21 Writing NN index file to temp file /var/folders/43/bxszl5k54ml3ljc8rqdcc7vchdgq7y/T//RtmpKUSSVQ/file56b764e6d078
## 01:38:21 Searching Annoy index using 1 thread, search_k = 3000
## 01:38:22 Annoy recall = 100%
## 01:38:22 Commencing smooth kNN distance calibration using 1 thread
## 01:38:22 Initializing from normalized Laplacian + noise
## 01:38:22 Commencing optimization for 500 epochs, with 107586 positive edges
## 01:38:25 Optimization finished